Multifractal extension of detrended cross‐correlation analysis (DCCA ) usually involves the trouble that the computation of arbitrary powers of the negative cross‐covariances leads to complex values . However , a commonly adopted modulus processing method MFDXA often indicates significant multifractal cross‐correlation signal when actually no fractality exists . Mulitfractal cross‐correlation analysis (M FCCA) proposed by O s′ wiecimka preserves the sign of the cross‐covariances and settles the trouble above .M FCCA is a natural general extension of MFDFA and DCCA . Here it was demonstrated that M FCCA performs more effectively and powerfully than M FDXA from the view of the general two‐component ARFIMA processes model . MFCCA can correctly identify the signal of multifractality behavior and show sensitivity to the varying of the weight parameter W .%基于降趋交叉分析法（DCCA）的多重分形情形拓展存在麻烦点，即负的交叉协方差的任意矩可能会导致复值的出现．通常采取模的处理方法 M FDXA 会在实际没有分形特征情形下检测出明显的多重分形信号．Os′ wiecimka 提出的多重分形降趋交互相关性分析法（MFCCA ）保留了每个子区间降趋协方差符号这一重要信息，解决了上述麻烦点，同时能够准确识别多重分形交互关系信号，是降趋交互相关性分析法的自然拓展．这里从一般形式两成分 ARFIMA 模型的角度出发，证明了 M FCCA 算法相比 M FDXA 算法更加有效．MFCCA 能够正确地识别分形特征，同时对权重参数 W 表现出一定的敏感性．此外，将 M FCCA 算法应用于中国股票市场上，证实了 CSI 300指数量价间只有大的波动才具有分形特征．
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